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Technical Briefs

Fuzzy Model Identification With Enhanced Validity Criterion for Mechanical System Design

[+] Author and Article Information
Kuo-Ho Su

 Graduate Institute of Digital Mechatronic Technology, Chinese Culture University, 55, Haw-Kang Rd., Yang-Ming-Shan, Taipei 11114, Taiwan, R.O.C.sgh@faculty.pccu.edu.tw

J. Mech. Des 133(10), 104501 (Sep 27, 2011) (7 pages) doi:10.1115/1.4004483 History: Received December 31, 2010; Accepted May 12, 2011; Published September 27, 2011

Model identification for machine system design, design optimization, and manufacturing planning is an important method that has high prediction accuracy and could become an essential stage in practical applications. In this paper, an effective fuzzy model identification algorithm for mechanical system design is developed. First, a fuzzy c-regression model clustering algorithm, in which hyperplane-shaped cluster representatives are utilized to provide a mathematical tool to partition the input–output space reasonably, is introduced. Then, an enhanced cluster validity criterion, in which the structural information hidden in the clusters can be reflected in the index, is proposed to choose the optimal number of clusters. In the proposed architecture, an improved Takagi–Sugeno fuzzy model is proposed to describe the system. Two illustrative examples under various conditions are provided, and their performances are indicated in comparison with other published works. In comparison to these fuzzy works, the proposed fuzzy model identification requires fewer fuzzy rules and a shorter tuning time.

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Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 1

Flow chart of the FCRM clustering algorithm

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Figure 2

Flow chart of the fuzzy model identification algorithm using the FCRM

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Figure 3

(a) Results of Bezdek’s partition coefficient νPC versus c for case 1; (b) results of the proposed enhanced cluster index CI versus c for case 1

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Figure 4

(a) Results of Bezdek’s partition coefficient νPC versus c for case 2; (b) results of the proposed enhanced cluster index CI versus c for case 2

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Figure 5

Plot of CI versus c in Example 2

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Figure 6

Simulated results of Example 2: (a) input signal u(k); (b) unforced system g(k); (c) outputs of the identification model (dashed line) and the plant (solid line); (d) error between the model output and the plant output

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